International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
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Predictive Battery Health Monitoring Using Offline –Trained Random Forest Models and IOT Integration DEEPTI RAI1, AKSHAT JAIN2, ADMYA JAISWAL3, AYUSH PATEL4, EPSHA NETY5, KARNIKA DUBEY6 1Assistant Professor, Department of Electrical Engineering, Shri Govindram Seksaria Institute of Technology &
Science, Indore M.P
2,3,4,5,6 UG Students, Department of Electrical Engineering, Shri Govindram Seksaria Institute of Technology &
Science, Indore M.P. ------------------------------------------------------------------***-----------------------------------------------------------------
Abstract- Advancing the prospects of eco-friendly
(Battery Management System) comes into play. A BMS is an electronic system that manages and monitors a rechargeable battery, either at the cell or pack level, by gathering and transmitting important data on the battery's state to an external interface where users can access it. This data includes measurements such as current, voltage, temperature, and coulomb count, which are used to assess the health of the battery and take necessary actions to protect it from harm. BMS are crucial for preventing overcharging, overheating and deep discharge. Proper battery monitoring improves efficiency, safety and longevity of Li-Ion batteries. [1]. A Battery Management System (BMS) plays an essential role in monitoring key parameters such as battery voltage, current, State of Charge (SoC), and State of Health (SoH). The precision of monitoring will ensure that the battery works effectively and also, at the required time, gives a signal to the user regarding whether it needs some sort of service or replacement [2]. By using IoT and Machine Learning, it is now possible to collect vast amounts of data about a battery, such as its current state, temperature, and charge/discharge cycles. This data canthen be used to develop a predictive model which can be used to predict the future performance of the battery and provide insights into how to optimize battery use.
transportation tends to correlate with optimizing energy management. This paper describes the design and implementation of a smart BMS which utilizes ML and IoT technologies for real-time monitoring and predicting of battery performance metrics.
The methodology used involves estimating the State of Charge (SoC) and State of Health (SoH) of lithium-ion batteries with pre-trained Random Forest and Decision Tree models that draw from historical datasets and incorporate voltage, current, and capacity metrics. Unlike adaptive learning systems, this model is offline, eliminating the need for frequent retraining while still ensuring prompt and trusted predictions. In its simplest form, an Arduino Uno microcontroller acts as a data acquisition unit that gathers data from the sensors to be sent to cloud. The processed data is displayed on a web dashboard which serves as an interface giving the user real time data on the battery performance. This approach to battery monitoring offers an unprecedented level of responsiveness, flexibility, and affordability which improves the upkeep, lifespan, and safety of the battery positioning it as a fundamental shift in the construction in smart energy-efficient EV systems.
IoT-based battery analytics can provide an effective way to monitor and manage battery performance. By using sensors to collect real-time data, it is possible to track the health of the battery and receive alerts when it is time to replace it. Additionally, this data can be used to develop predictive models that can be used to forecast the future performance of the battery and improve its overall efficiency. [3] Machine Learning is also an important tool for battery analytics. By using machine learning algorithms, it is possible to analyze the data collected from the sensors and identify patterns and trends that can be used to improve battery performance. For example, machine learning algorithms can be used to identify the
Keywords: Machine Learning, SoC, SoH, RuL
1. INTRODUCTION In today's era, we are surrounded by electronics devices like the toothbrush we first touch in the morning to the mobile we use to even the car we drive. Everything runs on a power source like batteries and in fact mostly these batteries are rechargeable Li Ion batteries. The charging and discharging over a long period affects the health of these batteries and it becomes very important to know the health of batteries for safety purposes. That’s when BMS
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